



import datasets

from .evaluate import evaluate


_snake_case						=    '\\n@inproceedings{Rajpurkar2016SQuAD10,\n  title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n  author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n  booktitle={EMNLP},\n  year={2016}\n}\n'

_snake_case						=    '\nThis metric wrap the official scoring script for version 1 of the Stanford Question Answering Dataset (SQuAD).\n\nStanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by\ncrowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,\nfrom the corresponding reading passage, or the question might be unanswerable.\n'

_snake_case						=    '\nComputes SQuAD scores (F1 and EM).\nArgs:\n    predictions: List of question-answers dictionaries with the following key-values:\n        - \'id\': id of the question-answer pair as given in the references (see below)\n        - \'prediction_text\': the text of the answer\n    references: List of question-answers dictionaries with the following key-values:\n        - \'id\': id of the question-answer pair (see above),\n        - \'answers\': a Dict in the SQuAD dataset format\n            {\n                \'text\': list of possible texts for the answer, as a list of strings\n                \'answer_start\': list of start positions for the answer, as a list of ints\n            }\n            Note that answer_start values are not taken into account to compute the metric.\nReturns:\n    \'exact_match\': Exact match (the normalized answer exactly match the gold answer)\n    \'f1\': The F-score of predicted tokens versus the gold answer\nExamples:\n\n    >>> predictions = [{\'prediction_text\': \'1976\', \'id\': \'56e10a3be3433e1400422b22\'}]\n    >>> references = [{\'answers\': {\'answer_start\': [97], \'text\': [\'1976\']}, \'id\': \'56e10a3be3433e1400422b22\'}]\n    >>> squad_metric = datasets.load_metric("squad")\n    >>> results = squad_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'exact_match\': 100.0, \'f1\': 100.0}\n'



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION  ,   _KWARGS_DESCRIPTION)
class     UpperCAmelCase_       (			datasets.Metric):







							def   snake_case__ (  self):

														'''simple docstring'''

														return datasets.MetricInfo(
														    description=_DESCRIPTION,     citation=_CITATION,     inputs_description=_KWARGS_DESCRIPTION,     features=datasets.Features(
														        {
														            "predictions": {"id": datasets.Value("string"), "prediction_text": datasets.Value("string")},
														            "references": {
														                "id": datasets.Value("string"),
														                "answers": datasets.features.Sequence(
														                    {
														                        "text": datasets.Value("string"),
														                        "answer_start": datasets.Value("int32"),
														                    }),
														            },
														        }),     codebase_urls=["https://rajpurkar.github.io/SQuAD-explorer/"],     reference_urls=["https://rajpurkar.github.io/SQuAD-explorer/"],     )







							def   snake_case__ (  self,     __a,     __a):

														'''simple docstring'''

														_lowerCAmelCase	:				Tuple					  =       {prediction["id"]: prediction["prediction_text"] for prediction in predictions}
														_lowerCAmelCase	:				List[str]					  =       [
														    {
														        "paragraphs": [
														            {
														                "qas": [
														                    {
														                        "answers": [{"text": answer_text} for answer_text in ref["answers"]["text"]],
														                        "id": ref["id"],
														                    }
														                    for ref in references
														                ]
														            }
														        ]
														    }
														]
														_lowerCAmelCase	:				Optional[Any]					  =       evaluate(dataset=__a,     predictions=__a)
														return score


import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_fnet import FNetTokenizer
else:
		lowercase    :   List[Any]						=			None

lowercase    :   Tuple						=			logging.get_logger(__name__)
lowercase    :   Optional[Any]						=			{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

lowercase    :   str						=			{
    'vocab_file': {
        'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/spiece.model',
        'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json',
        'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json',
    },
}

lowercase    :   int						=			{
    'google/fnet-base': 512,
    'google/fnet-large': 512,
}

lowercase    :   str						=			'▁'




class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	_A								=	VOCAB_FILES_NAMES
	_A								=	PRETRAINED_VOCAB_FILES_MAP
	_A								=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_A								=	['input_ids', 'token_type_ids']
	_A								=	FNetTokenizer







	def __init__(   self						:Optional[int]	,  a						:Union[str, Any]=None	,  a						:Optional[int]=None	,  a						:str=False	,  a						:Tuple=True	,  a						:Optional[Any]=True	,  a						:Optional[int]="<unk>"	,  a						:List[str]="[SEP]"	,  a						:Optional[Any]="<pad>"	,  a						:int="[CLS]"	,  a						:Dict="[MASK]"	,  **a						:Optional[int]	,  )						->					List[str]:
								# Mask token behave like a normal word, i.e. include the space before it and
								# is included in the raw text, there should be a match in a non-normalized sentence.
								__UpperCamelCase      :  List[Any]       =   (
								    AddedToken(a	,  lstrip=a	,  rstrip=a	,  normalized=a		)
								    if isinstance(a	,  a		)
								    else mask_token
								)

								super().__init__(
								    a	,  tokenizer_file=a	,  do_lower_case=a	,  remove_space=a	,  keep_accents=a	,  unk_token=a	,  sep_token=a	,  pad_token=a	,  cls_token=a	,  mask_token=a	,  **a	,  )

								__UpperCamelCase      :  List[Any]       =   do_lower_case
								__UpperCamelCase      :  str       =   remove_space
								__UpperCamelCase      :  Dict       =   keep_accents
								__UpperCamelCase      :  Union[str, Any]       =   vocab_file
								__UpperCamelCase      :  List[str]       =   False if not self.vocab_file else True







	def   _lowerCamelCase							(   self						:Dict	,  a						:List[int]	,  a						:Optional[List[int]] = None		)						->					List[int]:
								__UpperCamelCase      :  List[str]       =   [self.sep_token_id]
								__UpperCamelCase      :  List[str]       =   [self.cls_token_id]
								if token_ids_a is None:
															return cls + token_ids_a + sep
								return cls + token_ids_a + sep + token_ids_a + sep







	def   _lowerCamelCase							(   self						:Union[str, Any]	,  a						:List[int]	,  a						:Optional[List[int]] = None		)						->					List[int]:
								__UpperCamelCase      :  List[Any]       =   [self.sep_token_id]
								__UpperCamelCase      :  List[Any]       =   [self.cls_token_id]

								if token_ids_a is None:
															return len(cls + token_ids_a + sep		) * [0]
								return len(cls + token_ids_a + sep		) * [0] + len(token_ids_a + sep		) * [1]







	def   _lowerCamelCase							(   self						:int	,  a						:str	,  a						:Optional[str] = None		)						->					Tuple[str]:
								if not os.path.isdir(a		):
															logger.error(f'Vocabulary path ({save_directory}) should be a directory'		)
															return
								__UpperCamelCase      :  str       =   os.path.join(
								    a	,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

								if os.path.abspath(self.vocab_file		) != os.path.abspath(a		):
															copyfile(self.vocab_file	,  a		)

								return (out_vocab_file,)